Effects of Hydropeaking on Nearshore Habitat Use and Growth of Age‐0 Rainbow Trout in a Large Regulated River
Bibliographic record
Abstract
Abstract We evaluated the effects of hourly variation in flow caused by power load following at Glen Canyon Dam (“hydropeaking”) on the nearshore habitat use and growth of age‐0 rainbow trout Oncorhynchus mykiss downstream from the dam in the Colorado River, Arizona. Reduction in the extent of hydropeaking is a common element of restoration efforts in regulated rivers, but empirical support for such a practice is limited. Our assessment was based on a comparison of abundance in shoreline areas determined by electrofishing at different flows as well as analysis of otolith microstructure. The catch rates of age‐0 rainbow trout in nearshore areas were at least two‐ to fourfold higher at the daily minimum flow than at the daily maximum, indicating that most age‐0 rainbow trout do not maintain their position within immediate shoreline areas when flows are high. A striping pattern, identified by the presence of atypical daily increments formed every 7 d, was evident in over 50% of the 259 otoliths examined in 2003 but in only 6% of the 334 examined in 2004. The weekly pattern corresponded to a reduction in the extent of hourly flow fluctuations on Sundays during the growing season, which occurred in 2003 but not in 2004. The atypical increments were 25% wider than the adjacent increments and were indicative of significant ( F 15, 235 = 19.2, P < 0.0001) short‐term increases in otolith growth. The somatic growth rate among fish with otoliths where striping was present (11.2 mm/month) was slightly greater than that among fish with otoliths without striping (10.8 mm/month), but the difference was not significant. We provide evidence suggesting that otolith growth improved on Sundays in 2003 because it was the only day of the week when most age‐0 fish were found in immediate shoreline areas, where higher water temperatures and lower velocities provided better growing conditions.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".